Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Image Classification Using a Combination of Convolutional Layers and Restricted Boltzmann Machines
KTH, School of Engineering Sciences (SCI).
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [sv]

Denna studie har till syfte att undersöka vilken effekt restricted Boltzmann machines (RBMs) har när de kombineras med ett convolutional neural network (CNN) som används för bildklassificering. Detta är ett intressant område som kombinerar övervakad och oövervakad träning av neurala nätverk och som ännu inte har granskats ordentligt.

Olika versioner av neurala nätverk tränades och testades med hjälp av två dataset bestående av 70 000 handskrivna siffror respektive 60 000 naturliga bilder. Utgångspunkten var ett vanligt CNN där första lagret sedan byttes ut mot två olika sorters RBMs. För att evaluera effekten av RBMs jämfördes felprocent och träningstid.

Resultaten visar att kombinationen av RBMs och CNNskan fungera om rätt implementerad och användas i tillämpningar. Det finns fortfarande mycket kvar att undersöka, då denna studie begränsades av den tillgängliga beräkningskraften.

Abstract [en]

This study aims to investigate what impact restricted Boltzmann machines (RBMs) have when combined with a convolutional neural network (CNN) used for image classification. This is an interesting area of research which combines supervised and unsupervised training of neural networks and it has not been thoroughly examined yet.

Different versions of neural networks were trained and tested using two datasets consisting of 70 000 handwrittendigits and 60 000 natural images. The starting point was aregular CNN where the first layer then was replaced by two different kinds of RBMs. To evaluate the effect of RBMs the error rates and training times were compared.

The results show that the combination of RBMs and CNNs can work if implemented right and can be used in different applications. There is still much left to investigate, since this study was limited by the available computational power.

Place, publisher, year, edition, pages
2015. , 31 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-168005OAI: oai:DiVA.org:kth-168005DiVA: diva2:813822
Supervisors
Examiners
Available from: 2015-05-25 Created: 2015-05-25 Last updated: 2015-05-25Bibliographically approved

Open Access in DiVA

fulltext(1128 kB)719 downloads
File information
File name FULLTEXT01.pdfFile size 1128 kBChecksum SHA-512
f64743bc28b3fc2ce22bb96f8ad751cd237a5f50dd4676e9ecfcd4816a4b5d84b9ff3aca288e5c7b2d54fdade5c8e784d5833b36df56982876c81542f0cb5ca3
Type fulltextMimetype application/pdf

By organisation
School of Engineering Sciences (SCI)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 719 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 1931 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf